Streaming media quality of experience prediction for network slice selection in 5G networks

ABSTRACT

Systems, methods and computer software are disclosed for predicting streaming media Quality of Experience (QoE) for network slice selection in 5G networks. In one embodiment, a method includes developing a mathematical model of QoE parameters at base station transmitters; providing feedback from the models to a Quality of Service (QoS) manager; and selecting, by the QoS manager based on the feedback from the models, a 5G network slice that meets QoS requirements for allocation to a user.

CROSS-REFERENCE TO RELATED APPLICATIONS

This application claims priority under 35 U.S.C. § 119(e) to U.S.Provisional Pat. App. No. 62/852,483, filed May 24, 2019, titled“Streaming Media Quality of Experience Prediction for Network SliceSelection in 5G Networks” which is hereby incorporated by reference inits entirety for all purposes. This application also hereby incorporatesby reference, for all purposes, each of the following U.S. PatentApplication Publications in their entirety: US20170013513A1;US20170026845A1; US20170055186A1; US20170070436A1; US20170077979A1;US20170019375A1; US20170111482A1; US20170048710A1; US20170127409A1;US20170064621A1; US20170202006A1; US20170238278A1; US20170171828A1;US20170181119A1; US20170273134A1; US20170272330A1; US20170208560A1;US20170288813A1; US20170295510A1; US20170303163A1; and US20170257133A1.This application also hereby incorporates by reference U.S. Pat. No.8,879,416, “Heterogeneous Mesh Network and Multi-RAT Node Used Therein,”filed May 8, 2013; U.S. Pat. No. 9,113,352, “HeterogeneousSelf-Organizing Network for Access and Backhaul,” filed Sep. 12, 2013;U.S. Pat. No. 8,867,418, “Methods of Incorporating an Ad Hoc CellularNetwork Into a Fixed Cellular Network,” filed Feb. 18, 2014; U.S. patentapplication Ser. No. 14/034,915, “Dynamic Multi-Access Wireless NetworkVirtualization,” filed Sep. 24, 2013; U.S. patent application Ser. No.14/289,821, “Method of Connecting Security Gateway to Mesh Network,”filed May 29, 2014; U.S. patent application Ser. No. 14/500,989,“Adjusting Transmit Power Across a Network,” filed Sep. 29, 2014; U.S.patent application Ser. No. 14/506,587, “Multicast and BroadcastServices Over a Mesh Network,” filed Oct. 3, 2014; U.S. patentapplication Ser. No. 14/510,074, “Parameter Optimization and EventPrediction Based on Cell Heuristics,” filed Oct. 8, 2014, U.S. patentapplication Ser. No. 14/642,544, “Federated X2 Gateway,” filed Mar. 9,2015, and U.S. patent application Ser. No. 14/936,267, “Self-Calibratingand Self-Adjusting Network,” filed Nov. 9, 2015; U.S. patent applicationSer. No. 15/607,425, “End-to-End Prioritization for Mobile BaseStation,” filed May 26, 2017; U.S. patent application Ser. No.15/803,737, “Traffic Shaping and End-to-End Prioritization,” filed Nov.27, 2017, each in its entirety for all purposes. This document alsohereby incorporates by reference U.S. Pat. Nos. 9,107,092, 8,867,418,and 9,232,547 in their entirety. This document also hereby incorporatesby reference U.S. patent application Ser. No. 14/822,839, U.S. patentapplication Ser. No. 15/828,427, U.S. Pat. App. Pub. Nos.US20170273134A1, US20170127409A1, US20190243836A1 in their entirety.

BACKGROUND

A conventional mobile core network delivers contents in accordance witha fixed Quality of Service (QoS) level assigned to the content. On theother hand, future 5G networks are expected to provide support for avariety of services. The challenge of 5G is to assure the networkperformance and QoS requirements of different services such as MachineType Communication (MTC), enhanced Mobile Broadband(eMBB) and UltraReliable Low Latency Communication (URLLC). To meet the requirements ofcomplex QoS of different applications and services, 5G networkstherefore should support various QoS capabilities including the accuratedetection of current QoS related events to trigger immediate actions andprediction of future QoS related events with high degree of accuracy.

Network slicing in 5G network provides dynamic programming capabilitiesfor QoS assurance. It is difficult to develop traditional software toschedule network resources amongst the slices, especially when there isno established causal relationship between network events and QoS. Henceit has been proposed to include a machine learning/analytics enginewhich will use historical data to help make predictions of QoE and aidin QoS selections through slicing.

SUMMARY

Systems and methods for predicting streaming media Quality of Experience(QoE) for network slice selection in 5G networks. The inventors havecontemplated the use of the described idea for all radio accesstechnologies. In one embodiment, a method may be disclosed forpredicting streaming media Quality of Experience (QoE) for network sliceselection in 5G networks includes developing a mathematical model of QoEparameters at base station transmitters; providing feedback from themodel to a Quality of Service (QoS) manager; and selecting, by the QoSmanager based on the feedback from the model, a 5G network slice thatmeets QoS requirements for allocation to a user.

In another embodiment, a non-transitory computer-readable mediumcontaining instructions for predicting streaming media Quality ofExperience (QoE) for network slice selection in 5G networks isdescribed. The instructions, when executed, cause a system to performsteps including developing a mathematical model of QoE parameters atbase station transmitters; providing feedback from the model to aQuality of Service (QoS) manager; and selecting, by the QoS managerbased on the feedback from the model, a 5G network slice that meets QoSrequirements for allocation to a user.

In another embodiment, a system may be disclosed for predictingstreaming media Quality of Experience (QoE) for network slice selectionin 5G networks is described. The system performs steps includingdeveloping a mathematical model of QoE parameters at base stationtransmitters; providing feedback from the model to a Quality of Service(QoS) manager; and selecting, by the QoS manager based on the feedbackfrom the model, a 5G network slice that meets QoS requirements forallocation to a user.

BRIEF DESCRIPTION OF THE DRAWINGS

FIG. 1 is a block diagram showing a system for managing QoS, inaccordance with some embodiments.

FIG. 2 is a diagram showing a multimedia streaming architecture, inaccordance with some embodiments.

FIG. 3 is a diagram showing overflow and underflow of buffers, inaccordance with some embodiments.

FIG. 4 is a state diagram of birth death-process, in accordance withsome embodiments.

FIG. 5 is a state diagram showing a birth causing overflow, inaccordance with some embodiments.

FIG. 6 is a diagram showing a frequency of changes within a slidingwindow, in accordance with some embodiments.

FIG. 7 is a block diagram showing data management and functions, inaccordance with some embodiments.

FIG. 8 is a diagram showing an intelligent data pipe, in accordance withsome embodiments.

DETAILED DESCRIPTION

Multimedia streaming requires buffering of data blocks at both basestations and on User equipment. Delivery of blocks on the transmitterside and playout on the receiver side, during a multimedia session,depends upon the wireless channel conditions. Since the channelconditions are completely random, it is possible that the Quality ofService level allocated to the user at the start of a session and isfixed throughout, may not be enough to meet the changing channelconditions through the session. This will result in an unsatisfactoryuser experience.

FIG. 1 shows a system 100 for managing QoS. The system 100 includes aservice layer 101 communicating with an SDN controller 102 and amanagement gateway 103. Also shown is QoS allocations to services 104.

Thus, QoS level is changed during the session by a slice managementcomponent which can either select a new or update an existing corenetwork slice. The orchestration of QoS amongst various network slicesduring a session is thus a requirement to enhance the user experience.Such a setup is useful also because operators try to optimize theirresource usage in such a way as to maximize Service Level Agreementfulfillment with minimum amount of resources possible.

Several works have proposed to automate QoS level updating based on thefeedback data received from the requesting UE during the contentdelivery session using an AI (Artificially Intelligent) engine. The AIcomponent uses historical data to construct models of a session and canautomatically classify the traffic into one of the many QoS levels.

AI engines develop statistical model of sessions by collecting variousnetwork parameters and performance data and carrying out statisticalanalysis. The models are then used to take decisions to improve theperformance and optimize the QoE.

Any kind of analytics-based framework for QoE prediction requires a highdegree of correlation between the performance data that is collected andQoS policy for slice orchestration. There are works available that havedeveloped models for determination of QoE in terms of these performancemetrics.

However, any involvement of an AI engine in the base station or userequipment for QoE prediction is compute intensive if online training todetermine model parameters needs to be done for a multimedia session andif accuracy of prediction is desired to be high. For example, aregression-based model of QoE in terms of performance metrics likebandwidth, Round trip time, jitter etc. will require a matrix inversion.Similarly using models like Convolutional Neural Nets and Deeplearning-based mechanism may also be used, in some embodiments, but isnot needed in all cases.

The invention, instead of or in addition to using compute intensivestatistical models, proposes to develop a mathematical model of QoErelated parameters at Base Station transmitters and through feedback toQoS manager aid in selecting QoS policy in a session.

Mathematical Model of media playout and transmit buffer

FIG. 2 illustrates a typical multimedia streaming architecture:

Transmitter transmits the frames that arrive from Core Network everytransmission time interval. The transmit blocks arrive the transmitbuffer 201 and are put in a fixed length queue for a user, until theyare scheduled to be transmitted. Packet are transmitted by way of awireless channel 202 to a receiver 203. Scheduling of users isproportional fair, hence the transmit time can be divided into equalnumber of slots with the user scheduled for transmission at every timeslot.

Let A_(n) ∈ A={0,1, . . . S_(A)} denote the number of transmit blocksarriving for a user in time slot n and D_(n) ∈ D={0,1, . . . s_(D)} thenumber of transmit blocks dequeued in slot n. Due to Adaptive modulationand coding based on Channel Quality feedback from the receiver, theprocess of transmission is adaptive burst by burst. This makes bothenqueue and dequeue process a random sequence which is assumed to bei.i.d. (independent and identically distributed).

If Q_(n) denotes the queue length at the beginning of the slot n, thenthe dynamics of this process can be represented as:Q _(n+1)=max{Q _(n) −D _(n), 0}+A _(n)   (Equation 0)

Overflow and underflow of buffers during a session adversely effects theuser QoE. FIG. 3 shows a buffer 300 with underflow and overflowconditions. In order to keep the experience above a bearable threshold,the invention proposes to communicate buffer underflow/overflowprobability values to the QoS management entity which will then decidethe slice with appropriate QoS for upcoming transmit blocks. Theselection of the slice will update the bit rate of the content deliverythus bringing a marked improvement in the user QoE.

Buffering process can be modeled as a discrete time Markov ChainBirth-Death Process as explained below:

-   Enqueue Probability at state i={the probability of birth in state    i}=b_(i)=p_(i,i+1)-   Dequeue Probability at state i={the probability of death in state    i}=d_(i)=p_(i,i−1)-   No Enqueue/Dequeue at state i=Access of memory slot={the probability    being in state i}=a_(i)=p_(i,i)

Also, since the state at any time instant can be any of the three so wehave,a _(i) +b ₁ +d _(i)=1   (Equation 1)

FIG. 4 illustrates a state diagram of birth death-process 400. The nodesin the state diagram are the individual time slots during which the userqueue is accessed.

In the buffer allocation/deallocation process the maximum number ofnodes in the state diagram shown above is finite. This is because oflimitation on the buffer size.

The maximum buffer size ism=Bmax−Bmin   (Equation 2)

If the nodes are in state Bmax, any ‘birth’ at the next instance i.e.enqueue will lead to an overflow. Similarly, if the nodes in a state isBmin, any ‘death’ i.e. dequeue will cause an underflow.

In the next few paragraphs, the method to determine the probability ofthese events are evaluated. In sections that follow, a practical onlinemethod of calculating the ‘birth’ and ‘death’ probability is calculated.They are random events as both enqueuing and dequeuing processes arerandom in nature.

Let V be the state probability vector at any time instant whosecomponents are the probabilities of a slot in a buffer being occupied.Sum of all components being probabilities is equal to 1.Σ_(i) v _(i)=1   (Equation 3)

Thus, following equations hold true:v ₀ =a ₀ v ₀ +d ₁ v ₁   (Equation 4)v _(i) =b _(i−1) v _(i−1) +a _(i) v _(i) +d _(i+1) v _(i+1)   (Equation5)

Using the equations 1-4 above, we can evaluate the probability of ‘i’occupied at any time instant as follows:

$\begin{matrix}{v_{i} = {\prod\limits_{j = 1}^{i}\;{\frac{b_{j - 1}}{d_{j}}\mspace{14mu} v_{0}}}} & \left( {{Equation}\mspace{14mu} 6} \right)\end{matrix}$

Using Equation 2 we get,

$\begin{matrix}{v_{0} = \frac{1}{\Sigma_{i \geq 0}{\prod\limits_{j = 1}^{i}\;\frac{b_{j - 1}}{d_{j}}}}} & \left( {{Equation}\mspace{14mu} 7} \right)\end{matrix}$

For the special case where b_(i)=b(i≥0) and d_(i)=d(i≥1) for all ‘i’, wehave for finite length buffer m=Bmax−Bmin

$\begin{matrix}{{{v_{i} = {\left( \frac{b}{d} \right)^{i}\mspace{14mu} v_{0}}},{i = 0},1,{\ldots\mspace{14mu}.\mspace{14mu}.\mspace{14mu} m}}{And}} & \left( {{Equation}\mspace{14mu} 8} \right) \\{v_{0} = {\frac{1}{\sum\limits_{i = 0}^{m}\;\left( \frac{b}{d} \right)^{i}} = \frac{1 - \left( \frac{b}{d} \right)}{1 - \left( \frac{b}{d} \right)^{m + 1}}}} & \left( {{Equation}\mspace{14mu} 9} \right)\end{matrix}$

Evaluation of buffer overflow and underflow probability is shown in thestate diagram 400 of FIG. 4. Probability of a birth at the instance whennumber of slots occupied is maximum and is evaluated as follows:

$\begin{matrix}{P_{ov} = {{bv}_{m} = {{{b\left( \frac{b}{d} \right)}^{m}\frac{1 - \left( \frac{b}{d} \right)}{1 - \left( \frac{b}{d} \right)^{m + 1}}} = \frac{b^{m + 1}\left( {d - b} \right)}{d^{m + 1} - b^{m + 1}}}}} & \left( {{Equation}\mspace{14mu} 10} \right)\end{matrix}$

Above equation is interpreted as occurrence of overflow event whenmaximum number of slots are occupied and a “birth” or enqueue of a frameoccurs.

Similarly, as shown in the state diagram 500 of FIG. 5, underflowprobability is evaluated as:

$\begin{matrix}{P_{uf} = {{dv}_{0} = {{d\frac{1 - \frac{b}{d}}{1 - \left( \frac{b}{d} \right)^{m + 1}}} = \frac{d^{m + 1}\left( {d - b} \right)}{d^{m + 1} - b^{m + 1}}}}} & \left( {{Equation}\mspace{14mu} 11} \right)\end{matrix}$

Underflow occurs when no slots in the buffer are occupied and a “death”for dequeue occurs. Online evaluation of birth and death probabilitiesat the base station transmitter is considered. Only unknowns inequations 9 and 10 above are the probabilities of birth and death.

Even though both enqueueing and dequeuing processes are non-stationaryrandom processes, for small time intervals they can be assumed to bestationary.

The rate at which frames are dequeued will be decided by the feedbackabout the wireless channel state the transmitter receives from thereceiver. For small window or time interval, the rate of dequeue can beassumed to be a constant.

Since enqueue process is decided by QoS policy allocated to the currentmultimedia session, there is very little random variation in this rate.It can also be assumed to be a constant in a small window.

These assumptions help to evaluate the probabilities through a slidingwindow-based approach.

In order to evaluate the birth and death probability, the approach usedis to check change in queue length in the time slot allocated to user.The change in length of the queue is the difference in the rate ofdequeue and frame arrival rate.

Assume that the index of current time slot is n and current queue lengthis Q_(n) ∈ [B_(min), B_(max)].

For a given slot k, the change in queue length is given byI _(k) =D _(k) −A _(k)   (Equation 12)

As shown in diagram 600 of FIG. 6, the sliding window covers the N_wmost recent time slots scheduled for the user. In this window theobserved sequence of buffer length change is given by {I_1,I_2,I_3 . . .}. For window n, as shown in the figure below, the observation vector isW _(n) =[I _(n) , I _(n−1) , . . . I _(n−w+1)]  (Equation 13)

By noting down the frequency of reduction and increase in the window,the birth and death probability can be estimated. A reduction in queuelength in a slot corresponds to “death” or dequeue. Similarly, anincrease in queue length in a slot corresponds to “birth” or enqueue.

The frequency of these changes in the window is given by,N _(pos)=Σ_(k=n−N) _(w) ₊₁ ^(n)Pos(I _(k))   (Equation 14)where, function Pos(I_(k)) returns 1 if the change in queue length ispositive i.e. birth or enqueue.N _(neg)=Σ_(k=n−N) _(w) ₊₁ ^(n)Neg(I _(k))   (Equation 15)where, function Neg(I_(k)) returns 1 if the change in queue length isnegative i.e. death or dequeue.

Thus, probability of birth at the end of a window (beginning of a timeslot) is:

$\begin{matrix}{b = \frac{N_{pos}}{N_{w}}} & \left( {{Equation}\mspace{14mu} 16} \right)\end{matrix}$

And probability of death at the end of same window and beginning of atime slot is:

$\begin{matrix}{d = \frac{N_{neg}}{N_{w}}} & \left( {{Equation}\mspace{14mu} 17} \right)\end{matrix}$

These values along with the length of the buffer can be plugged intoEquations 10 and 11 to evaluate the overflow and underflow probabilityat the beginning of user's time slot.

Overflow and underflow probability values are then communicated to theQoS manager of a media gateway to select the appropriate network slicefor allocation to the user. Thus, it aids in slice and QoSorchestration.

The HDA solution architecture 700 is shown in FIG. 7. The architecture700 includes data sources 702. The data sources 702 in one embodimentinclude a HetNet Gateway 704, customer data 706 and external data 708.The HetNet Gateway 704 is a RAN management and virtualization node,described elsewhere herein and in the documents incorporated byreference into this document. The solution architecture 700 alsoincludes a data management and processing element 710 in communicationwith the data sources 702. The data management and processing element710 includes an HDA data lake 712. The HDA data lake includes anintelligent data pipe 714 providing an interface to the data sources, aswell as various data stores: a customer data store 716, a temporal datastore 718, an aggregate and KPI store 720, and an external data store724. A management element 722 is present to manage interconnectionsbetween the various data stores. The HDA data lake 712 also includes adata catalog 726, a security element 728 for ensuring securecommunications for all data stores based on per-data store policies, anddata services element 730 for interfacing with external user systems.The HDA solution architecture includes a user access element 732 forproviding external user services (see FIG. 4). The user access element732 includes, as examples, an operational dashboard 734, a report andanalysis portal 736, analytic workspaces 738, services and externalsystems 740 and SON and HNG 742. The HDA architecture 700 furtherincludes an elastic cloud platform 750, for providing extensible,virtualized infrastructure on a public or private cloud, andinfrastructure hardware 760, e.g., physical servers and networks. TheCNN described herein could run on the HDA architecture 700, in someembodiments. U.S. Pat. App. No. US20190243836A1 is hereby incorporatedby reference in its entirety for all purposes.

In some embodiments, a lightweight agent running in HNG 704 watchesavailability of new data and notifies the pipeline 714. A data pullprocess is initiated, get data from HNGs Each HNG instance has one ofthe lightweight agents installed and running. Data types at the HNGcould include: counters and stats collected at HNG; CWS locations,configuration parameters—Stats related to HW etc.; alarms and alerts;logs (HNG and CWS); configuration changes; backhaul measurements. Modelsas described herein could also interface with the HNG as describedherein to push or pull data from the UE or to the base station.

Referring now to FIG. 8, the intelligent data pipe 814 is shown. Theintelligent data pipe 814 is in communication with the HetNet Gateway804, customer data 806 and external data 808. The intelligent data pipe814 includes services 814 a and topics 814 b and provides and receivesdata from the remainder of the data lake. The intelligent data pipe alsoincludes scalable storage 814 c.

The intelligent data pipe 814 is an orchestrated set of processesdefined on-demand to bring in data streams to HDA for processing,provisioned to collect counters, data sets, transactions flowingexternally from devices, databases or streams. The intelligent data pipe814 provides several different types of functionality. These include theability to stream data from source to the sink; the ability to configureas a service on-demand from UI or CLI; the ability to support multipledata formats, such as JSON, CSV, XML; and the ability to attachlight-weight dynamic data processing services.

The topics 814 b of the intelligent data pipe 814 comprise highlyavailable queues for data to be written in, from external sources ordata lake. The attached in-line services 814 a may have ability forpattern recognition or writing data. The storage 814 c is a faulttolerant temporal storage attached to topics that caches data. Theservices 814 a comprise micro-services attached in-line to the topics torecognize patterns generating alerts or write data to the destinations.Topics 814 b would be used to implement the CNN functionality describedherein, in some embodiments.

The present disclosure contemplates the use of slice selection methodsfor the selection of various types of network slices, including: 5Gnetwork slices; network slices that are or include other RATs, such as2G/3G/4G/5G/Wi-Fi; network slices that include certain core networks ormultiple core networks; network slices that include or exclude certainRAN nodes; network slices that use different RAN node functional splitsat one or more RAN nodes for enhanced performance; network slices thatuse other QoS mechanisms such as DSCP, QoS, etc. to enable sliceperformance, including using other RATs; and other types of networkslices.

The present disclosure contemplates the use of virtualized managementgateways, including containerized management gateways. The presentdisclosure contemplates the use of a QoS manager that considers resourcelimitations and constraints across multiple RATs simultaneously,including UE support and RAN node support and backhaul constraints.

Computation of probability values may be performed at any node and maybe transmitted to the QoS management entity, in some embodiments.Computation may be performed at a data lake or in a data fog or at anedge compute node, in some embodiments. Computation may be performed atthe QoS management entity or a node colocated therewith, in someembodiments.

The probability values may be based on additional parameters in additionto the buffer parameters described herein. The probability values may becombined with other parameters at the QoS management entity, in someembodiments. In some embodiments the QoS management entity may be at agateway situated between the RAN and the core network; in someembodiments the QoS management entity may be located in the RAN or atthe base of a tower as a part of a functional split of a RAN node.

In some embodiments, the software needed for implementing the methodsand procedures described herein may be implemented in a high levelprocedural or an object-oriented language such as C, C++, C#, Python,Java, or Perl. The software may also be implemented in assembly languageif desired. Packet processing implemented in a network device caninclude any processing determined by the context. For example, packetprocessing may involve high-level data link control (HDLC) framing,header compression, and/or encryption. In some embodiments, softwarethat, when executed, causes a device to perform the methods describedherein may be stored on a computer-readable medium such as read-onlymemory (ROM), programmable-read-only memory (PROM), electricallyerasable programmable-read-only memory (EEPROM), flash memory, or amagnetic disk that is readable by a general or specialpurpose-processing unit to perform the processes described in thisdocument. The processors can include any microprocessor (single ormultiple core), system on chip (SoC), microcontroller, digital signalprocessor (DSP), graphics processing unit (GPU), or any other integratedcircuit capable of processing instructions such as an x86microprocessor.

The foregoing discussion discloses and describes merely exemplaryembodiments of the present invention. In some embodiments, softwarethat, when executed, causes a device to perform the methods describedherein may be stored on a computer-readable medium such as a computermemory storage device, a hard disk, a flash drive, an optical disc, orthe like. As will be understood by those skilled in the art, the presentinvention may be embodied in other specific forms without departing fromthe spirit or essential characteristics thereof. For example, wirelessnetwork topology can also apply to wired networks, optical networks, andthe like. The methods may apply to LTE-compatible networks, toUMTS-compatible networks, to 5G networks, or to networks for additionalprotocols that utilize radio frequency data transmission. Variouscomponents in the devices described herein may be added, removed, splitacross different devices, combined onto a single device, or substitutedwith those having the same or similar functionality.

Although the present disclosure has been described and illustrated inthe foregoing example embodiments, it is understood that the presentdisclosure has been made only by way of example, and that numerouschanges in the details of implementation of the disclosure may be madewithout departing from the spirit and scope of the disclosure, which islimited only by the claims which follow. Various components in thedevices described herein may be added, removed, or substituted withthose having the same or similar functionality. Various steps asdescribed in the figures and specification may be added or removed fromthe processes described herein, and the steps described may be performedin an alternative order, consistent with the spirit of the invention.Features of one embodiment may be used in another embodiment. Otherembodiments are within the following claims.

The invention claimed is:
 1. A method for predicting streaming mediaQuality of Experience (QoE) for network slice selection in 5G networks,the method comprising: developing a mathematical model of QoE parametersat base station transmitters; providing feedback from the models to aQuality of Service (QoS) manager; selecting, by the QoS manager based onthe feedback from the models, a 5G network slice that meets QoSrequirements for allocation to a user; and evaluating a buffer underflowprobability or a buffer overflow probability, wherein evaluating abuffer underflow probability or a buffer overflow probability includeschecking a change in queue length in a time slot allocated to a user. 2.The method of claim 1 wherein providing feedback from the models to theQoS manager comprises communicating buffer underflow/overflowprobability values.
 3. The method of claim 1 wherein the selecting ofthe slice includes updating a bit rate of the content delivery.
 4. Themethod of claim 1 wherein a buffer overflow event occurs when a maximumnumber of slots are occupied and an enqueue of a frame occurs.
 5. Themethod of claim 1 wherein a buffer underflow event occurs when no slotsin the buffer are occupied and a dequeue of a frame occurs.
 6. Anon-transitory computer-readable medium containing instructions forpredicting streaming media Quality of Experience (QoE) for network sliceselection in 5G networks, which, when executed, cause a system toperform steps comprising: developing a mathematical model of QoEparameters at base station transmitters; providing feedback from themodels to a Quality of Service (QoS) manager; selecting, by the QoSmanager based on the feedback from the models, a 5G network slice thatmeets QoS requirements for allocation to a user; and evaluating a bufferunderflow probability or a buffer overflow probability, whereinevaluating a buffer underflow probability or a buffer overflowprobability includes checking a change in queue length in a time slotallocated to a user.
 7. The non-transitory computer-readable medium ofclaim 6 further including instructions for providing feedback from themodels to the QoS manager comprises communicating bufferunderflow/overflow probability values.
 8. The non-transitorycomputer-readable medium of claim 6 further including instructionswherein the selecting of the slice includes updating a bit rate of thecontent delivery.
 9. The non-transitory computer-readable medium ofclaim 6 further including instructions wherein a buffer overflow eventoccurs when a maximum number of slots are occupied and an enqueue of aframe occurs.
 10. The non-transitory computer-readable medium of claim 6further including instructions wherein a buffer underflow event occurswhen no slots in the buffer are occupied and a dequeue of a frameoccurs.
 11. A system for predicting streaming media Quality ofExperience (QoE) for network slice selection in 5G networks, comprising:a plurality of base station transmitters; and a Quality of Service (QoS)manager; wherein a mathematical model of QoE parameters are developed atthe base station transmitters, wherein the models provide feedback tothe QoS manager, and wherein the QoS manager selects a 5G network slicethat meets QoS requirements for allocation to a use based on thefeedback from the models; and wherein a buffer underflow probability ora buffer overflow probability are evaluated, wherein a buffer underflowprobability or a buffer overflow probability are evaluated includeschecking a change in queue length in a time slot allocated to a user.12. The system of claim 11 wherein the feedback provided from the modelsto the QoS manager comprises buffer underflow/overflow probabilityvalues.
 13. The system of claim 11 wherein a buffer overflow eventoccurs when a maximum number of slots are occupied and an enqueue of aframe occurs.
 14. The system of claim 11 wherein a buffer underflowevent occurs when no slots in the buffer are occupied and a dequeue of aframe occurs.